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pubmed-article:2251287pubmed:dateCreated1991-1-16lld:pubmed
pubmed-article:2251287pubmed:abstractTextWe consider a randomly diluted higher-order network with noise, consisting of McCulloch-Pitts neurons that interact by Hebbian-type connections. For this model, exact dynamical equations are derived and solved for both parallel and random sequential updating algorithms. For parallel dynamics, we find a rich spectrum of different behaviors including static retrieving and oscillatory and chaotic phenomena in different parts of the parameter space. The bifurcation parameters include first- and second-order neuronal interaction coefficients and a rescaled noise level, which represents the combined effects of the random synaptic dilution, interference between stored patterns, and additional background noise. We show that a marked difference in terms of the occurrence of oscillations or chaos exists between neural networks with parallel and random sequential dynamics.lld:pubmed
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pubmed-article:2251287pubmed:authorpubmed-author:RossJJlld:pubmed
pubmed-article:2251287pubmed:authorpubmed-author:WangL PLPlld:pubmed
pubmed-article:2251287pubmed:authorpubmed-author:PichlerE EEElld:pubmed
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pubmed-article:2251287pubmed:volume87lld:pubmed
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pubmed-article:2251287pubmed:pagination9467-71lld:pubmed
pubmed-article:2251287pubmed:dateRevised2010-9-7lld:pubmed
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pubmed-article:2251287pubmed:year1990lld:pubmed
pubmed-article:2251287pubmed:articleTitleOscillations and chaos in neural networks: an exactly solvable model.lld:pubmed
pubmed-article:2251287pubmed:affiliationDepartment of Chemistry, Stanford University, CA 94305.lld:pubmed
pubmed-article:2251287pubmed:publicationTypeJournal Articlelld:pubmed
pubmed-article:2251287pubmed:publicationTypeResearch Support, U.S. Gov't, Non-P.H.S.lld:pubmed
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